Adaptive neural network control of an aerial work platform's arm

Jia, Pengxiao; Li, En; Liang, Zizhe; Yanhui, Qiang

doi:10.1109/wcica.2012.6359065

Cited by 5 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To realize the tracking control of work platform, a selftuning fuzzy PID controller is designed in Miao et al [1], and the adaptive neural network control scheme is proposed in Jia et al [2]. However, the elastic deformation of beam is ignored in these models established.…”

Section: Introductionmentioning

confidence: 99%

A Backstepping Controller with the RBF Neural Network for Folding‐Boom Aerial Work Platform

Song

et al. 2022

Complexity

View full text Add to dashboard Cite

Aerial work platform is a kind of engineering vehicle which is used for hoisting personnel to the appointed place for maintenance or installation. Based on the dynamics model considering the flexible deformation existing in the arm system of folding-boom aerial platform vehicle, this study presents a NN-based backstepping controller used for trajectory tracking control of work platform. The proposed controller can reduce tracking error of work platform and suppress the vibration simultaneously by using the RBF neural network system to compensate model uncertainties and disturbances. Furthermore, we prove that the whole system is stable and convergent by Lyapunov stability theorem. In addition, we give the simulation results which show that the good control performance of the designed controller for trajectory tracking and vibration inhabiting of work platform in the case of model uncertainties.

show abstract

Section: Introductionmentioning

confidence: 99%

A Backstepping Controller with the RBF Neural Network for Folding‐Boom Aerial Work Platform

Song

et al. 2022

Complexity

View full text Add to dashboard Cite

show abstract

“…To implement the control, one critical step is to determine the position and orientation of an aircraft. Jia et al (2012) used an adaptive neural network controller to control the aerial work platform's arm. Templeton et al (2007) used the Global Positioning Systems (GPS) data to position the aircraft, and they determined the orientation of a four-rotor aircraft using the data acquired by barometers and posture sensors.…”

Section: Introductionmentioning

confidence: 99%

Instrumentation and self-repairing control for resilient multi-rotor aircrafts

Wang

Qian

Song

et al. 2018

View full text Add to dashboard Cite

Purpose Even though multi-rotor aircrafts are becoming more and more prevalent in the fields of aerial photography, agricultural spraying, disaster searching and rescuing, how to achieve higher reliability and robustness of an aircraft still poses a big challenge. It is not a rare case that a multi-rotor aircraft is severely damaged or crushed when an actuator or sensor is malfunctioned. This paper aims at the resilience of an aircraft when a rotor is malfunctioned. Design/methodology/approach The reliability of a multi-rotor aircraft can be measured in terms of stability, robustness, resilience and fault tolerance. All of these four aspects are taken into consideration to improve overall reliability of aircrafts. When a rotor malfunction occurs, the control algorithm is cable of adjusting the operation conditions of the rest of rotors to achieve system stability. Findings In this paper, the authors first present a research topic on the development of a resilient multi-robot aircraft. A multi-rotor aircraft usually possesses more actuated motions than the required degrees of freedom. Originality/value The authors proposed to equip the multi-rotor aircraft with malfunction detecting sensors, and they developed the self-repairing algorithm to re-stabilize the aircraft when a malfunction of a rotor occurs. The design concept and methods were implemented on an eight-rotor aircraft, and the performance of the proposed instrumentation and self-repairing algorithm have been verified and validated.

show abstract

“…As the extensive use of light-long beam in the structure of arm system of aerial work platform, elastic deformation of beam cannot be ignored to guarantee work platform's stable motion. For realizing trajectory tracking of aerial work platform, adaptive neural network controller is adopted in Jia et al 2 and self-tuning fuzzy proportionalintegral-derivative (PID) control scheme is proposed in Miao et al 3 However, the deformation of beam is not considered in the established model. Based on the theory of flexible multi-body dynamics and Lagrange's equation, the model of folding-boom aerial work platform with flexible beam driven by hydraulic cylinder is established, and the vibration existed in flexible beam is shown in Hu et al 1 Besides, the similar model is obtained, and fuzzy PID is used for the trajectory tracking of work platform in Meng, 4 but this study only gives simulation results, and the stability of system is not proved.…”

Section: Introductionmentioning

confidence: 99%

A neural network–based sliding mode controller of folding-boom aerial work platform

Cai

Cui

et al. 2017

Advances in Mechanical Engineering

View full text Add to dashboard Cite

Aerial work platform is a special vehicle for carrying personnel to the appointed site in the air for operations. Therefore, the work platform requires high stability. This article proposes a sliding mode controller based on neural network for tracking control of folding-boom aerial work platform. Since the chattering caused by sliding mode controller with high-speed switching control may lead to system performance degradation, continuous control obtained from neural network system replaces discontinuous switching control to eliminate chattering. Furthermore, the whole system is proved to be stable by Lyapunov stability theorem. Finally, numerical results show that the designed controller can eliminate the chattering resulting from switching control in sliding mode controller and inhibit the vibration of work platform when there exists system uncertainty. Moreover, the controller is effective for the reduction of tracking error.

show abstract

Adaptive neural network control of an aerial work platform's arm

Cited by 5 publications

References 12 publications

A Backstepping Controller with the RBF Neural Network for Folding‐Boom Aerial Work Platform

A Backstepping Controller with the RBF Neural Network for Folding‐Boom Aerial Work Platform

Instrumentation and self-repairing control for resilient multi-rotor aircrafts

A neural network–based sliding mode controller of folding-boom aerial work platform

Contact Info

Product

Resources

About